Research data supporting "Quantitative monitoring and modelling of retrodialysis drug delivery in a brain phantom"
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Rognin, E., Fox, N., & Daly, R. (2022). Research data supporting "Quantitative monitoring and modelling of retrodialysis drug delivery in a brain phantom" [Dataset]. https://doi.org/10.17863/CAM.88917
The data was generated as part of the EPSRC IRC in Targeted Delivery for Hard-to-Treat Cancers <https://www.teddy.eng.cam.ac.uk/>. In this work we present a platform which can be used to investigate and optimise the performance of microdialysis probes in the context of drug delivery. Our detailed experimental rig and the software supplied with our work is aimed at facilitating the transition of this delivery technique to clinical trials. Above all, our work is a significant contribution to the field as we can rapidly image and measure drug concentration far from the probe and discuss the size and shape of the drug plume. The images are taken with the following setup: a collimated light source (CX0202-WHIIC, Edmund Optics) shines parallel beams through the brain phantom. A telecentric objective lens (TitanTL 0.184X, Edmund Optics) is mounted on a digital camera (Basler Ace acA1920-150um, 2/3" 10-bit monochromatic sensor). A pass-band filter (FB500-40, Thorlabs) centred around 500 nm, with a bandwidth of 40 nm is mounted at the back of the lens. Images are recorded at 1 frame per minute with a fixed exposure time for each series (in the range 9 ms to 11 ms). Images are processed using Python environment and open-source packages. The microdialysis probe used in this study is the 70 MD Bolt Catheter, from Mdialysis. The brain phantom is an agarose gel 0.6% in Dulbecco's phosphate-buffered saline. The model drug solution is methylene blue 0.2 mg/mL in DPBS.
Raw images are stored in zip files which must be unzipped first before running data analysis scripts. The software is a set of self-documented IPython Notebook files. The use of these scripts requires the following to be installed: - Python 3.7+ - Jupyter Notebook - Python modules: numpy, skimage, matplotlib, ipywidgets, tqdm, pywt, natsort, abel For each experimental folder, three scripts must be run sequentially: 1. ``1 Preprocessing.ipynb`` loads raw images and applies filtering. The output is absorbance fields (``A.npy`` Python Numpy file) and probe mask (``mask.npy``). Coordinates of the area of interest are stored in Python file ``cuvette.pickle``. 2. ``2 Integrate.ipynb`` measures the mass seen in the image stack discarding any probe shadow. The output is a text file ``mass.txt``. 3. ``3 Reconstruct.ipynb`` computes concentration fields using the inverse Abel transform and applies a correction to the total mass measured. The output is a text file ``mass_Abel.txt``, and concentration fields (optional).
brain cancer, local drug delivery, microdialysis
Engineering and Physical Sciences Research Council (EPSRC), grant no. EP/S009000/1
This record's DOI: https://doi.org/10.17863/CAM.88917
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/